Delete custom_interface.py

custom_interface.py

@@ -1,127 +0,0 @@
-import torch
-from speechbrain.pretrained import Pretrained
-
-class WhisperASR(Pretrained):
-    """A ready-to-use Whisper ASR model
-
-    The class can be used  to run only the encoder (encode()) to run the entire encoder-decoder whisper model
-    (transcribe()) to transcribe speech. The given YAML must contains the fields
-    specified in the *_NEEDED[] lists.
-
-    Example
-    -------
-    >>> from speechbrain.pretrained.interfaces import foreign_class
-    >>> tmpdir = getfixture("tmpdir")
-    >>> asr_model = foreign_class(source="hf", 
-    ... pymodule_file="custom_interface.py", 
-    ... classname="WhisperASR",
-    ... hparams_file='hparams.yaml', 
-    ... savedir=tmpdir,
-    ... )
-    >>> asr_model.transcribe_file("tests/samples/example2.wav")
-    """
-
-    HPARAMS_NEEDED = ['language']
-    MODULES_NEEDED = ["whisper", "decoder"]
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.tokenizer = self.hparams.whisper.tokenizer
-        self.tokenizer.set_prefix_tokens(self.hparams.language, "transcribe", False)
-        self.hparams.decoder.set_decoder_input_tokens(
-            self.tokenizer.prefix_tokens
-    )
-
-    def transcribe_file(self, path):
-        """Transcribes the given audiofile into a sequence of words.
-
-        Arguments
-        ---------
-        path : str
-            Path to audio file which to transcribe.
-
-        Returns
-        -------
-        str
-            The audiofile transcription produced by this ASR system.
-        """
-        waveform = self.load_audio(path)
-        # Fake a batch:
-        batch = waveform.unsqueeze(0)
-        rel_length = torch.tensor([1.0])
-        predicted_words, predicted_tokens = self.transcribe_batch(
-            batch, rel_length
-        )
-        return predicted_words
-
-    def encode_batch(self, wavs, wav_lens):
-        """Encodes the input audio into a sequence of hidden states
-
-        The waveforms should already be in the model's desired format.
-        You can call:
-        ``normalized = EncoderDecoderASR.normalizer(signal, sample_rate)``
-        to get a correctly converted signal in most cases.
-
-        Arguments
-        ---------
-        wavs : torch.tensor
-            Batch of waveforms [batch, time, channels].
-        wav_lens : torch.tensor
-            Lengths of the waveforms relative to the longest one in the
-            batch, tensor of shape [batch]. The longest one should have
-            relative length 1.0 and others len(waveform) / max_length.
-            Used for ignoring padding.
-
-        Returns
-        -------
-        torch.tensor
-            The encoded batch
-        """
-        wavs = wavs.float()
-        wavs, wav_lens = wavs.to(self.device), wav_lens.to(self.device)
-        encoder_out = self.mods.whisper.forward_encoder(wavs)
-        return encoder_out
-
-    def transcribe_batch(self, wavs, wav_lens):
-        """Transcribes the input audio into a sequence of words
-
-        The waveforms should already be in the model's desired format.
-        You can call:
-        ``normalized = EncoderDecoderASR.normalizer(signal, sample_rate)``
-        to get a correctly converted signal in most cases.
-
-        Arguments
-        ---------
-        wavs : torch.tensor
-            Batch of waveforms [batch, time, channels].
-        wav_lens : torch.tensor
-            Lengths of the waveforms relative to the longest one in the
-            batch, tensor of shape [batch]. The longest one should have
-            relative length 1.0 and others len(waveform) / max_length.
-            Used for ignoring padding.
-
-        Returns
-        -------
-        list
-            Each waveform in the batch transcribed.
-        tensor
-            Each predicted token id.
-        """
-        with torch.no_grad():
-            wav_lens = wav_lens.to(self.device)
-            encoder_out = self.encode_batch(wavs, wav_lens)
-            predicted_tokens, scores = self.mods.decoder(encoder_out, wav_lens)
-            predicted_words = self.tokenizer.batch_decode(
-                predicted_tokens, skip_special_tokens=True)
-            if  self.hparams.normalized_transcripts:
-                predicted_words = [
-                        self.tokenizer._normalize(text).split(" ")
-                        for text in predicted_words
-                    ]
-
-            
-        return predicted_words, predicted_tokens
-
-    def forward(self, wavs, wav_lens):
-        """Runs full transcription - note: no gradients through decoding"""
-        return self.transcribe_batch(wavs, wav_lens)